# 堆叠去噪自动编码机在多参数估计领域的应用
import argparse
from typing import Dict
from apps.fmcw.ias.mpe_sdae_model import MpeSdaeModel

class MpeSdaeApp(object):
    def __init__(self):
        self.name = 'apps.fmcw.mpe_sdae_app.MpeSdaeApp'

    @staticmethod
    def startup(params:Dict = {}) -> None:
        print(f'堆叠去噪自动编码机雷达多参数估计 v0.0.1')
        input_dim = 784  # 假设我们使用的是28x28的图像
        hidden_dim1 = 128
        hidden_dim2 = 64
        latent_dim = 32
        model = MpeSdaeModel(input_dim, hidden_dim1, hidden_dim2, latent_dim)
        criterion = nn.MSELoss()  # 使用均方误差作为损失函数
        optimizer = optim.Adam(model.parameters(), lr=1e-3)
        # 编写训练过程？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？

    @staticmethod
    def train(model, data_loader, epochs):
        model.train()
        for epoch in range(epochs):
            for data, _ in data_loader:
                # 添加噪声
                noisy_data = add_noise(data.view(data.size(0), -1))
                # 前向传播
                output = model(noisy_data)
                loss = criterion(output, data.view(data.size(0), -1))
                # 反向传播
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
            
            print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')
        # 假设data_loader是一个PyTorch DataLoader，包含了你的训练数据
        # train(model, data_loader, epochs=50)

    @staticmethod
    def test(model, data_loader):
        model.eval()
        with torch.no_grad():
            for data, _ in data_loader:
                noisy_data = add_noise(data.view(data.size(0), -1))
                output = model(noisy_data)
                # 这里可以添加代码来可视化原始图像、噪声图像和重建图像
                # ...
        # 假设test_data_loader是一个PyTorch DataLoader，包含了你的测试数据
        # test(model, test_data_loader)




def main(params:Dict = {}) -> None:
    MpeSdaeApp.startup(params=params)

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--run_mode', action='store',
        type=int, default=1, dest='run_mode',
        help='run mode'
    )
    return parser.parse_args()

if '__main__' == __name__:
    args = parse_args()
    params = vars(args)
    main(params=params)